fdasrvf (version 1.9.4)

joint_gauss_model: Gaussian model of functional data using joint Model

Description

This function models the functional data using a Gaussian model extracted from the principal components of the srvfs using the joint model

Usage

joint_gauss_model(warp_data, n = 1, no = 5)

Arguments

warp_data

fdawarp objecet from time_warping of aligned data

n

number of random samples (n = 1)

no

number of principal components (n=4)

Value

Returns a fdawarp object containing

fs

random aligned samples

gams

random warping function samples

ft

random function samples

qs

random srvf samples

References

Tucker, J. D., Wu, W., Srivastava, A., Generative Models for Function Data using Phase and Amplitude Separation, Computational Statistics and Data Analysis (2012), 10.1016/j.csda.2012.12.001.

Jung, S. L. a. S. (2016). "Combined Analysis of Amplitude and Phase Variations in Functional Data." arXiv:1603.01775 [stat.ME].

Examples

# NOT RUN {
data("simu_warp")
out1 = joint_gauss_model(simu_warp,n = 10)
# }